Low-Complexity Particle Swarm Optimization for Time-Critical Applications

Sohail, Muhammad; Saeed, Muhammad Omer Bin; Rizvi, Syed Z.; Shoaib, Mohammed; Sheikh, A.U.H.

doi:10.48550/arxiv.1401.0546

Cited by 5 publications

(6 citation statements)

References 43 publications

(67 reference statements)

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“…Remark 1. Although particle swarm optimization is a popular stochastic optimization method, it may not always provide low-complexity routing algorithms [45,46]. Authors in [47] compared the performance of the particle swarm optimization and differential evolution techniques to find delivery routings with minimum travel distances with quadratic complexity in time and space.…”

Section: Complexity Of the Auas Swarm Routing Algorithmmentioning

confidence: 99%

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

et al. 2022

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The emerging concept of drone swarms creates new opportunities with major societal implications. However, future drone swarm applications and services pose new communications and sensing challenges, particularly for collaborative tasks. To address these challenges, in this paper, we integrate sensor arrays and communication to propose a mathematical model to route a collection of autonomous unmanned aerial systems (AUAS), a so-called drone swarm or AUAS swarm, without having a base station of communication but communicating with each other using multiple spatio-temporal data. The theories of structured matrices, concepts in multi-beam beamforming, and sensor arrays are utilized to propose a swarm routing algorithm. We address the routing algorithm’s computational and arithmetic complexities, precision, and reliability. We measure bit-error-rate (BER) based on the number of elements in sensor arrays and beamformed output of the members of the swarm to authenticate and secure the routing for the decentralized AUAS networking. The proposed model has the potential to enable future drone swarm applications and services. Finally, we discuss future work on obtaining a machine-learning-based low-cost drone swarm routing algorithm.

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Section: Complexity Of the Auas Swarm Routing Algorithmmentioning

confidence: 99%

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

et al. 2022

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“…We recall here that particle swarm optimization has gained more attention as a stochastic optimization technique to route drone swarms. However, it does not consistently yield routing algorithms with low complexity [1,20,62]. On the other hand, the paper [63] shows the performance comparisons of particle swarm optimization and differential evolution techniques while identifying delivery routes, with minimal travel distances having quadratic complexity in both time and space.…”

Section: Arithmetic Complexity Of the Algorithmmentioning

confidence: 99%

“…Remark 1. Since N is fixed after the deployment of the drone swarms, i.e., the number of elements in the antenna array is fixed after the deployments, and followed by the Proposition 2, the RSwarm algorithm has just less than the quadratic complexity, i.e., O(M p ), where p < 2, while comparing with [1,20,[62][63][64][65][66]. Furthermore, the RSwarm algorithm allows one to route a drone swarm with over 100 members, which is an improvement compared to the limitations of the existing AODV-based swarm members [15,59,60].…”

Section: Arithmetic Complexity Of the Algorithmmentioning

confidence: 99%

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Myers,

Perera,

McLewee

et al. 2024

Drones

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The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

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“…This process continues until a termination condition is met. The number of iterations required for convergence depends on various factors such as the problem complexity, the size of the search space, and the convergence speed of the swarm [ 76 ]. Generally, the time complexity of PSO is considered to be moderate, as it typically requires a reasonable number of iterations to converge to an acceptable solution [ 77 ].…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

Khan,

Shafi,

Khawaja

et al. 2023

Sensors

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Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO.

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Low-Complexity Particle Swarm Optimization for Time-Critical Applications

Cited by 5 publications

References 43 publications

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Integrating Communication and Sensor Arrays to Model and Navigate Autonomous Unmanned Aerial Systems

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Adaptive Filtering: Issues, Challenges, and Best-Fit Solutions Using Particle Swarm Optimization Variants

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